hier.part {hier.part} | R Documentation |
Partitions variance in a multivariate dataset
hier.part(y, xcan, family = "gaussian", gof = "RMSPE", barplot = TRUE)
y |
a vector containing the dependent variables |
xcan |
a dataframe containing the n independent variables |
family |
family argument of glm
|
gof |
Goodness-of-fit measure. Currently "RMSPE", Root-mean-square 'prediction' error, "logLik", Log-Likelihood or "Rsqu", R-squared |
barplot |
If TRUE, a barplot of I and J for each variable is plotted expressed as percentage of total explained variance. |
This function calculates goodness of fit measures for the entire
hierarchy of models using all combinations of N independent variables
using the function all.regs
. It takes the list of goodness
of fit measures and, using the partition
function, applies the
hierarchical partitioning algorithm of Chevan and Sutherland (1991)
to return a simple table listing each variable, its independent
contribution (I) and its conjoint contribution with all other variables
(J).
Note earlier versions of hier.part (<1.0) produced a matrix and barplot of percentage distribution of effects as a percentage of the sum of all Is and Js, as portrayed in Hatt et al. (2004) and Walsh et al. (2004). This version plots the percentage distribution of independent effects only. The sum of Is equals the goodness of fit measure of the full model minus the goodness of fit measure of the null model.
The distribution of joint effects shows the relative contribution of each variable to shared variability in the full model. Negative joint effects are possible for variables that act as suppressors of other variables (Chevan and Sutherland 1991).
At this stage, the partition routine will not run for more than 12 independent variables. This function requires the gtools package in the gregmisc bundle
a list containing
gfs |
a data frame or vector listing all combinations of independent variables in the first column in ascending order, and the corresponding goodness of fit measure for the model using those variables |
IJ |
a data frame of I, the independent and J the joint contribution for each independent variable |
I.perc |
a data frame of I as a percentage of total explained variance |
The function produces a minor rounding error for analyses with more than than 9 independent variables. To check if this error affects the inference from an analysis, run the analysis several times with the variables entered in a different order. There are no known problems for analyses with 9 or fewer variables.
Chris Walsh Chris.Walsh@sci.monash.edu.au using c and fortran code written by Ralph Mac Nally Ralph.MacNally@sci.monash.edu.au.
Chevan, A. and Sutherland, M. 1991 Hierarchical Partitioning. The American Statistician 45, 90–96.
Hatt, B. E., Fletcher, T. D., Walsh, C. J. and Taylor, S. L. 2004 The influence of urban density and drainage infrastructure on the concentrations and loads of pollutants in small streams. Environmental Management 34, 112–124.
Mac Nally, R. 2000 Regression and model building in conservation biology, biogeography and ecology: the distinction between and reconciliation of 'predictive' and 'explanatory' models. Biodiversity and Conservation 9, 655–671.
Walsh, C. J., Papas, P. J., Crowther, D., Sim, P. T., and Yoo, J. 2004 Stormwater drainage pipes as a threat to a stream-dwelling amphipod of conservation significance, Austrogammarus australis, in south-eastern Australia. Biodiversity and Conservation 13, 781–793.
#linear regression of log(electrical conductivity) in streams #against seven independent variables describing catchment #characteristics (from Hatt et al. 2004) data(urbanwq) env <- urbanwq[,2:8] hier.part(urbanwq$lec, env, fam = "gaussian", gof = "Rsqu") #logistic regression of an amphipod species occurrence in #streams against four independent variables describing #catchment characteristics (from Walsh et al. 2004) data(amphipod) env1 <- amphipod[,2:5] hier.part(amphipod$australis, env1, fam = "binomial", gof = "logLik")